predict_tensor_cpp

This function calls the underlying C++ function `predict_tensor_cpp`
to compute predicted responses given a flattened tensor, low-rank
coefficient matrices, and scalar covariate coefficients.

Provides methods for low-rank tensor regression with tensor-valued predictors
and scalar covariates. Model estimation is performed using stochastic optimization
with random-walk updates for low-rank factor matrices. Computationally intensive
components for coefficient estimation and prediction are implemented in C++ via
'Rcpp'. The package also includes tools for cross-validation and prediction error
assessment.

Ritwick Mondal

TensorMCMC

Tensor Regression with Stochastic Low-Rank Updates

predict_tensor_cpp function

<dl><dt>X_vec</dt>
<dd>Flattened tensor (numeric vector of length n*p*d)</dd>
<dt>beta</dt>
<dd>List of p×d matrices representing tensor coefficients</dd>
<dt>gam</dt>
<dd>Numeric vector of scalar coefficients</dd>
<dt>n</dt>
<dd>Number of observations</dd>
<dt>p</dt>
<dd>Number of rows in each beta matrix</dd>
<dt>d</dt>
<dd>Number of columns in each beta matrix</dd>
<dt>rank</dt>
<dd>Rank of tensor decomposition</dd></dl>

Arguments

Predict Response Using Tensor Regression C++ — predict_tensor_cpp

<dl>

<dt>X_vec</dt>
<dd>Flattened tensor (numeric vector of length n*p*d)</dd>


<dt>beta</dt>
<dd>List of p×d matrices representing tensor coefficients</dd>


<dt>gam</dt>
<dd>Numeric vector of scalar coefficients</dd>


<dt>n</dt>
<dd>Number of observations</dd>


<dt>p</dt>
<dd>Number of rows in each beta matrix</dd>


<dt>d</dt>
<dd>Number of columns in each beta matrix</dd>


<dt>rank</dt>
<dd>Rank of tensor decomposition</dd>

</dl>

predict_tensor_cpp: Predict Response Using Tensor Regression C++

Description

Usage

Value

Arguments